Measuring impact of education and socio-economic factors on Health for Pakistan

Dr.Zahid Asghar1, Nazia Attique2, Amena Urooj3

ABSTARCT

Education affects health not because of the knowledge and practices one

can learn at school, but rather it shapes individuals life and can alter the

characteristics of an individual to be healthier. Measurement of health is

an abstract concept and health itself is affected by a number of factors.

This study aims at exploring whether there is any relation in education,

gender, and health for Pakistan. Exploratory data analysis and ordinal

logistic regression are used here to assess relationship between health,

education and other socio-economic factors. It is evident that individuals

with higher education level tend to have better health status than a

person with lower levels of education. There is also evidence of gender

being an important determinant of health in Pakistan. The variation in

health status of individuals is studied with reference to gender, culture,

occupations, and social economic status. This study provides a useful

piece of information for the policy makers in health and education

sectors. The data used in this study was collected by Pakistan and

Medical Research Council under National Health Survey of Pakistan

(NHSP 1990-1994).

1. Introduction

It is a common understanding that people with higher level of education lead a more healthy

life due to their enhanced level of awareness compared to the less educated individuals. Two

important prerequisites for an effective health policy are; monitoring and forecasting the

population's health and its health determinants. Health of any individual or that of a society or

community is not dependent on a particular single factor. In fact it is the product of the

interaction of our environments, socio-economic status, psycho-social conditions and cultural

norms and beliefs with our genetic inheritance. “The social conditions, in which people live,

1 Dr. Zahid Asghar is Assistant Professor at Statistics Department, Quaid-e-Azam University, Islamabad. 2 Nazia Attique is M. Phil student at Quaid-e-Azam University, Islamabad. 3 Amena Urooj is Staff Demographer at the Pakistan Institute of Development Economics, Islamabad.

powerfully influence their chances to be healthy. Indeed factors such as poverty, social

exclusion and discrimination, poor housing, unhealthy early childhood conditions and low

occupational status are important determinants of most diseases, deaths and health

inequalities between and within countries”(WHO, 2004).

The research on the subject reveals that people belonging to different socio-economic groups

experience different levels of health, whereas the factors that lead to different health

conditions need to be identified. (Wilkinson and Marmot, 2003)

The ‘social determinants’ are the socio economic conditions of the people which determine

their health. WHO and other health organizations have identified these determinants; which

are illustrated in Figure 1.

Figure 1: Determinants of health

Studies reveal that schooling is associated with several non-market outcomes. Among these

non-market returns to schooling, there has recently been a growing interest in the health

returns and is believed that besides human capital, health capital also emerges from the

education. So it is important to analyze whether education policies help to improve health.

The subject study is aimed at estimating the effect of education on health in particular and

exploring the relation between health and some other social factors in general in Pakistan.

The study has two main objectives; first to elucidate and analyze the effect of education,

gender, occupation etc. on health; second to understand the mechanism, by which education,

gender and other socio-economic factors can profoundly affect the health status of an

individual.

Keeping the objectives of the study we focus on the relation from socio-economic factors to

health by applying general linear model in multivariate framework. This is of tremendous

importance for our understanding of determinants of health as well as for our understanding

of how schooling affects and shape individual lives.

The study is outlined as: In section two brief review of the issue is discussed. Section three

describes the data and the methodological frame work applied in the study. Section four

consists of the exploratory data analysis of the variables. Section five is about the use of

ordinal logistic models, along with the empirical analysis of the estimation techniques used,

and finally we conclude the study.

2. Review of Literature

Since long, it has been observed and documented that the educational differences have great

impact on health status. Grossman (1972, 1975) has deeply explored the correlation between

education and health. Over the decades, a number of important mechanisms are proposed

through which direct or indirect influence of schooling on health can be studied. The impact

of past health on current health and years of formal schooling is studied by Grossman (1975)

where he uses a recursive model to identify the causal relationship between education and

health. In his model, health capital is measured in terms of Self Reported Health (SRH) and it

is shown that with past health, keeping other variables constant, schooling has a positive and

significant effect on present health.

Health disparities between better and less well educated people often increase when a new

health technology is introduced. Health disparities between better and less well educated

people often increase when a new health technology is introduced (Case, 2001).

Treating schooling as endogenous to health suggests that most of the correlation between

schooling and health is attributable to unobserved heterogeneity except possibly at low levels

of schooling for individuals with low cognitive ability (Christopher and Sidhu, 2005). They

also identify the role of cognitive ability in the health education relation and shows that both

schooling and ability are strongly associated with health at low levels but less related or

unrelated at high levels. Arendt (2001) analyzed the extent to which heterogeneity in health

and endogeneity of education explained the gradient in health. By making use Self Reported

Health (SRH), Body Mass Index (BMI) and indicators for high blood pressure and never

been smoking, he shows that education is related to SRH when controlling for the three other

health measures, which can be interpreted as inputs in health production. The diverse

demographic and socio-economic conditions and the availability of educational facilities

affect the extent of heterogeneity in health and health related quality of life. Shumueli (2003)

decomposed the demographic and socioeconomic factors to study this heterogeneity.

The complex multidimensional structure of health has been of keen interest for researcher

since years. Using the demographic and socio-economic factors, attempts of identifying the

complex relation of health and education have been made. Fuchs (2004) observes that there

are considerable uncertainties concerning the socio-economic correlates of health, the extent

to which they reflect causal chains and their implications for policy and studies the possible

reasons for this uncertainty. The inequality in health from the perspective of socio-economic

factors is analyzed in a study by Syed, et al. (2006), in which he has considered two ethnic

groups and has found a large diversity of SRH and prevalence of diabetes and distress among

the ethnic groups.

The possibility of a causal relationship between education and health is explored by (Arendt,

2005). Along with SRH, the study includes BMI and an indicator of never been smoking as

supplemental outcomes. The study shows that education is associated with better SRH for

both men and women. In an attempt to investigate the direct relationship between education

and health, Cutler and Lleras (2007) find that better educated individuals have more positive

health outcomes even after controlling for job characteristics, income and family background.

Ardent (2008) articulates this causal relationship in terms of hospitalization and finds the

significant effect of increase in education on decrease in hospitalization especially for

females. Evidence for a causal relationship running from better schooling to better health can

be found in an investigation conducted by Silles (2009). In which by relying on changes in

educational participation caused by raising the school the minimum school- leaving age, also

provides evidence of the causal effect of schooling on health.

Cutler, et al. (2005) described that the link between social status and health as complex,

perhaps too complex for a single explanation. Discussing the direct causal mechanisms

running from income to health, they have pointed that the link between income and health is

a result of the latter causing the former rather than the reverse. There is most likely a direct

positive effect of education on health but there are no well stated causal mechanisms.

Hartong and Osterbeek (1998) have studied the returns to education in terms of health status,

financial wealth and happiness, and have concluded that IQ independently affects health

status, even after controlling for schooling. Returns to education have also been calculated by

relating the value of health gain to the average income per capita (Groot and Brink, 2007).

The effect of education on health is analyzed by giving some tests for causality, and control

for unobserved heterogeneity; it is found that of gender, the education and the number of

years of education have a positive effect on the quality of education.

Cutler and Richardson (1998) measured the change in health capital by age, gender, race and

income and concluded that measuring changes in health by income or education is more

difficult than by measuring it by race and gender. More insight in the size of the quality of

health effect can be obtained by relating the value of the health gain to the average income

per capita.

Costa, L. and Uchôa, E. (2004) determined the factors associated with self-rated health

among adults, considering five dimensions of socio demographic variables. And it has been

observed that self-rated health among older adults is multidimensional in structure, being

influenced by socioeconomic conditions, social support, health status (with emphasis on

mental health), and access /use of healthcare services but not by the life style.

3. Data Description

The data used in this study is collected under National Health Survey of Pakistan (NHSP

1990-1994). It is a cross sectional survey which comprise of sample of size 19862 collected

randomly all over Pakistan. The survey uses three separate questionnaires for children, adult

male and adults female simultaneously. It gives detailed Information on several health profile

of individual and provides a base for the analysis of determinations of health especially

education.

In the NHSP (1990-94), the respondent’s health is measured in term of self reported health

(SRH) which is measured on ordinal scale having five categories as excellent, very good,

good, fair, and poor. The other variables involved in this study are education level,

occupation, social status, age, gender, marital status, residence and province.

SRH is subjective in the medical sense of being a state perceptible to the individual and not to

those who observe or examine the individual. It is not subjective in the psychological sense of

being moodily introspective or illusory. It reports something real, but directly observable only

by the individual reporting (Mirowsky, 2003). In the last three decades, self reported health

has been used increasingly as a measure in the psychological and gerontological areas, as

well as in epidemiological surveys (Lima Costa and Fernanda uchao, 2004). Self reported

health shows good reliability and its validity is equivalent to that of other more complex

measures of health status (Idler and Benyamini, 1997). BMI is a reliable indicator of total

body fat, which is related to the risk of diseases and death. It's a useful, indirect measure of

body composition because it correlates highly with body fat in most people. Weight in

kilograms is divided by height in meters squared (kg/m2).

The across the sample distribution of the variables used is given below which is supported by

a detailed descriptive analysis of these variables in section 4.

Table 1: The Profile of the Study Population (based on NHSP 1990-94) Variables Description Health Variables Subjective health

We take SRH graded as (1:excellent, 2:very good, 3:good, 4:fair, 5:poor) Objective health Arthritis, asthma, diabetes, hemorrhoids, tuberculosis, heart disease, pain in back, pain in knees, vision problem and dental problem

Schooling Variables For schooling five different levels of education are taken. i. Less Than Primary ii. Primary But Less Than Middle iii. Middle But Less Than Matric iv. Matric But Less Than degree v. Degree and Above

Social Status It is divided into only two categories high and low

Occupation It includes seven categories employed, self-employed unemployed, work in home, student, disabled and other

Other Background Variables

Age Age is defined from 20 up to 90 years

Gender Gender is classified as males and females

Marital Status There are three categories, single, married and other

Residence Type It consists of two categories urban and rural.

Provinces Punjab, Sindh, NWFP and Balochistan.

Table 1-a: Socio-demographic characteristics of the sample population with descriptive statistics, NHSP (1990-94)

Variables Percentage

Variables Percentage

Variables Percentage

Self reported health

Education level Age groups

Excellent 0.5 < primary 7.6 20-29 years 42.7

Very good 9.2 Primary but < middle

31.4 35-39 years 27.2

Good 37.6 Middle but < matric 20.5 40-49 years 15.4

Fair 35.2 Matric but < degree 31.1 50-59 years 9.4

Poor 17.5 Degree and above 9.4 60 years and above

5.3

Social status Gender Marital status

High 57.2 Male 70.6 Single 25

Low 42.8 Female 29.4 Married 72

Occupation Residence Others 3

Employed 24.7 Urban 56.5

Self Employed 28.5 Rural 43.5

Unemployed 1.7 Provinces

Student 4.5 Punjab 50.9

work in home/ Sick 22.7 Sindh 22.8

Disability 3 NWFP

19.9

Other 14.9 Balochistan 6.5

Figure 2: Percent of persons reporting SRH

It seems that now-a-days, people are better aware off about their health and due to several

possible factors majority groups and segmentations of the society are believed to be in

weaker conditions. As evident from the whole sample being surveyed that most of the people

report their health as ‘fair’ or ‘poor’. About 33 percent people report their health as ‘good’

and only 6 percent (approx) have very good or ‘excellent’ health. This overall distribution

highlights the poor condition of health in the society and emphasizes the need of attention in

this regard. Hence, it is desired to identify the factors which lead to low health. Therefore,

now discussing it in detail, we come across some interesting results related to the relation

between education and health.

4. Exploratory Analysis

Studying the relationship between the health and education by visually describing the data

reveals several interesting aspects. It seems from the graphical display that education, gender

play significant role in determining health. There is large variation in the categories of SRH.

Figure 3: Percent of persons reporting SRH Figure 4: Percent of persons reporting SRH by age by Gender

0.8%0.0%

1.0% 1.1% 0.8%

8.0%6.7%

37.5%36.4%

35.0%

38.0%37.4%

6.4%7.9%

11.6%

37.3%35.6%

33.9%

31.7%

36.2%

25.8%

16.2%16.8%

18.8% 19.1%

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

40.0%

20-29 years 30-39 years 40-49 years 50-59 yeasr > 60 years

Age Groups

Excellent

Very Good

Good

Fair

Poor

0.7% 0.5%

10.8%

5.1%

42.7%

22.8%

34.5%

38.4%

11.3%

33.2%

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

40.0%

45.0%

Male Female

Gender

Excellent

Very Good

Good

Fair

Poor

According to figure 3, there is quite large variation among the five categories of SRH over all

age groups. However, the category of ‘good’ and ‘fair’ remain stable and almost similar for

all age groups. Most of the people from the sample report their health as ‘good’ and ‘fair’.

The share of reporting health as ‘poor’ only rises significantly for the people above 60 years

of age. From figure 4, the response of men and women to the SRH reveals significant the

gender differentials. The high bars of ‘fair’ and ‘poor’ categories in SRH of females indicate

that women report themselves to be in a worse health condition as compared to the men. This

may be due to several possible factors/ reasons need to be identified with strong evidence.

Figure 5: Percent of persons reporting SRH Figure 6: Percent of persons reporting SRH by Residence by Province

0.8% 0.4%

10.4%

7.6%

36.3%

37.8%

35.0%

36.5%

17.6% 17.6%

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

40.0%

Urban Rural

Area of Residence

Excellent

Very Good

Good

Fair

Poor

0.2% 1.1% 1.6%2.0%

23.7%

5.9%

20.5%

61.1%

50.0%

44.9%

49.2%

19.5%

36.8%

28.2%

12.5%14.3%

17.9%

5.6% 5.2%

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

Punjab Sindh NWFP Balochistan

Provinces

Excellent

Very Good

Good

Fair

Poor

There is a lot of economic and social disparity in urban and rural population but here we do

not find any significant pattern in urban- rural area. However, the response pattern is entirely

different across the provinces as evident from figure 5 and 6. It might be due to the lack of

awareness about health that a large proportion of sample from Sindh declare them to be in

good health. While, major proportion of people from Punjab identifies them to be in the low

categories of health. However, evidence is needed to justify this argument.

Table 2: Subjective quality of health by education and gender Education Self Reported Health

Excellent (%)

Very Good (%) Good (%) Fair

(%) Poor (%)

Women < Primary - - 27.7 34 38.3 Primary but < Middle

- 1.9 14.6 36.8 46.7

Middle but < Matric

0.9 3.4 21.4 42.7 31.6

Matric but <degree

- 9.8 29.9 37.4 23

Degree and Above

4.9 12.2 34.1 43.9 4.9

Total 0.5 5.1 22.8 38.4 33.2 Men <Primary - 2.8 52.8 31.1 13.2 Primary but <Middle

- 8.5 38 39.2 14.3

Middle but <Matric

0.3 5.3 43.9 38.6 11.9

Matric but <degree

1.6 12.8 41.7 34.3 9.6

Degree and Above

1.2 27 49.1 17.2 5.5

Total 0.7 10.9 42.8 34.3 11.3 Note: the estimates are based on using study sample (I)

The cross tabulation of SRH with respect to education and gender reveals some interesting

behavior patterns. It is evident from the table that majority of men reported good to excellent

health. However, the majority of females lie in the category of fair and poor.

Now we consider the association between education and some more objective measures of

health; the self-reported number of chronic conditions. The percentages are shown in Table 3.

Table 3: Objective health status by Education

It seems prevalence of the disease or conditions is not same for different levels of education.

From the table it can be seen that the individuals having education up to degree level or

above have reported lesser diseases as compared to the less educated.

On the other hand, we observe that the ratio of reporting different diseases by individuals

having primary or less education is comparatively lower than the other education levels till

matric. This may be due to the reason that due to less education and lack of awareness they

fail to understand or realize the health problems they face. Some other reasons e.g. financial

factors may also play their underlying role here. In general educated persons report the

chronic conditions less frequently than less educated persons.

4.1 Comparison of Different Patterns of Self Reported Health by Education and Social

Status

Now in order to study the effect of education on health status, we make comparison by

analyzing the pattern of SRH across educational levels. But as noted earlier the response

behavior of men and women are entirely different suggesting that the gender differentials

impart a significant effect on health. On comparing the patterns of SRH at different health

Objective Health Education (values are in %)

< Primary Primary but < Middle Middle but < Matric

Matric but < degree Degree and Above

Arthritis 9.9 45.6 19.8 19.8 4.8

Asthma 9.3 33.3 16.0 32.0 9.3

Diabetes 8.2 26.5 20.4 36.7 8.2

Hemorrhoids 5.5 38.4 23.2 24.4 8.5

Tuberculosis 18.8 28.1 15.6 37.5 0.0

Heart disease 7.3 29.3 19.5 29.3 14.6

Pain in back 10.2 41.9 18.6 23.8 5.4

Pain in knees 9.8 42.9 20.9 21.2 5.3

Vision Problem 6.0 35.8 22.0 26.8 9.4

Dental Problem 9.0 33.6 20.1 29.4 7.8

status, we observe that with the increase in education women are getting more awareness

about health as indicated from the decreasing percentage of females under poor social status

reporting to be in very good health. However, the pattern is not similar in case of men. The

pattern is clear from the figures 8 and 9 respectively.

Figure 8: Pattern of SRH Very Good vs. Poor Figure 9: Pattern of SRH Very Good vs. poor for Women for Men

01.9 3.4

9.812.2

38.3

46.7

31.6

23

4.9

0

5

10

15

20

25

30

35

40

45

50

Less Than Primary

Primary But Less Than Middle

Middle But Less Than Matric

Matric But Less Than degree

Degree and Above

Level Of Education

Per

cen

t

Very Good

Poor

2.8

8.5

5.3

12.8

27

13.214.3

11.99.6

5.5

0

5

10

15

20

25

30

Less Than Primary

Primary But Less Than Middle

Middle But Less Than Matric

Matric But Less Than degree

Degree and Above

Level Of EducationP

erce

nt

Very Good

Poor

In figure 10 and 11 we find that as the level of education increases especially above middle there

is a very sharp decline in reporting poor health, this decline is steeper in females than in men.

However, the increase in reporting very good health is quite gradual for women but is very sharp

for men having matric or higher education.

Figure 10: Pattern of SRH Good vs. Poor Figure 11: Pattern of SRH Good vs. Poor for Women for Men

27.7

14.6

21.4

29.9

34.1

38.3

46.7

31.6

23

4.9

0

5

10

15

20

25

30

35

40

45

50

Less Than Primary

Primary But Less Than Middle

Middle But Less Than Matric

Matric But Less Than degree

Degree and Above

Level Of Education

Per

cen

t

Good

Poor

52.8

38

43.941.7

49.1

13.2 14.311.9

9.65.5

0

10

20

30

40

50

60

Less Than Primary

Primary But Less Than Middle

Middle But Less Than Matric

Matric But Less Than degree

Degree and Above

Level Of Education

Per

cen

t

Good

Poor

Even in case of declaring their health as good improves with the increase in education. In

figure 10 and 11, on comparing the categories of good vs. poor in SRH, we find very

significant role of education in women indicated by sharp increase in good health supported

by a sharp decline in reporting poor health among females having middle or higher education.

However, the behavior in men here remains quite stable and the role of education generating

awareness etc. is not evident here.

Figure 12: Pattern of SRH Fair vs. Poor Figure 13: Pattern of SRH Fair vs. Poor for Women for Men

3436.8

42.7

37.4

43.9

38.3

46.7

31.6

23

4.9

0

5

10

15

20

25

30

35

40

45

50

Less Than Primary

Primary But Less Than Middle

Middle But Less Than Matric

Matric But Less Than degree

Degree and Above

Level Of Education

Per

cen

t

Fair

Poor

31.1

39.2 38.6

34.3

17.2

13.2 14.311.9

9.6

5.5

0

5

10

15

20

25

30

35

40

45

Less Than Primary

Primary But Less Than Middle

Middle But Less Than Matric

Matric But Less Than degree

Degree and Above

Level Of Education

Per

cen

t

Fair

Poor

Similarly, reporting health as fair increases with the increase in education among female. But

remarkably, it declines among men. Hence, we may conclude that education plays a

significant role in generating awareness about health.

4.2 Self Reported Health by Body mass index

Now the percentage distribution of the respondents for SRH by body mass index is given as

under:-

Figure 14: SRH reporting of persons belonging to four categories of BMI

0.3 0.7 1.1

5.5

9.7 9.6

38.4 39.137.5

42.2

12.1

33.134.9

32.034.9

23.7

16.2 15.713.3

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

40.0

45.0

Underweight Normal Overweight Obese

BMI

Per

cen

t

Excellent Very Good Good Fair Poor

Figure 14 shows the percentage distribution of SRH for each level of BMI. The first thing we

observe that though response to an “excellent” health status is at minimal level i.e. 0.3%,

0.7% and 1.1%, but it exist from under weight to over weight respectively. At the same time

among obese nobody reports the state of excellent health. Further it seems that there is a

gradual increase in the percentage response in favor of “very good” health status. But for the

obese it again declines. The overweight category shows the maximum response of the

individuals (39.1%), of having “good” health. Again we see that this response increases from

underweight up to overweight but again for obese it decrease. So, without the loss of

generality, we can conclude here that on the basis of BMI vs. SRH, we can say that reporting

different categories of health is close to reality and the general awareness about health has

improved. The interesting point here is that people do not consider obesity a disease as

evident by the highest bar of ‘fair’ health at the sate of obese under BMI.

4.3 Body mass index by Education

Now we see the distribution of BMI for only two groups, one is highly educated in our

sample and other is poorly educated.

Figure 15: Percent of persons reporting on BMI scale by education

21.60

54.9

5.9

59.3

17.612.1

25.6

3

0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

Underweight

Normal

Overweight Obese

BMI

Per

cen

t

Less Than Primary Degree and Above

From the figure15 it is clear that usually less educated appears to report high level of

underweight conditions as compared to more educated people. Moreover they are also more

obese than the educated persons. About 60% of the highly educated persons are healthy.

Ignoring some fluctuations these patterns suggest almost negative relationship exist between

SRH and BMI, and in BMI and education i.e. with the increase in education people become

more aware of their health and hence, we can observe significant change in reporting BMI

and health status.

Hence, Not only has the general awareness about health had improved enabling them report

much better about their health but it also has improved the health conditions as indicated by

BMI, that educated people are more concerned about their health and physic.

4.4 Health care utilization and Education

We also investigate whether there is any link between health care behaviors and education or

not. So for this purpose we make comparison between literates and illiterates. We also

illustrate in figure 16 the behavior of individuals with in the literate category only. This is

based on the response of the persons when they are asked at the time of interview that

whether they seek any medical care in the last two weeks before the survey.

Figure 16: Percent of persons seeking Figure 17: Percent of persons seeking medical care by Literacy medical care by Levels of Education

IlliterateLiterate

23.30%

24.80%

22.50

23.00

23.50

24.00

24.50

25.00

Per

cen

t

29 .10

26 .20

26 .90

23 .20

20 .10

0.00 10.00 20.00 30.00

Percent

Less Than Primary

Primary But Less Than Middle

Middle But Less Than Matric

Matric But Less Than degree

Degree and Above

Lev

els

of E

du

catio

n

From figure 17, we see that with the increase in level of education, the frequency of seeking

any medical care decreases. 29 percent people with education level less than primary seek the

medical care, the percentage reduces gradually with an increase in education level and 20

percent with education degree and above seek the medical care.

5. Empirical Model and Estimation

After having descriptive analysis we use ordinal logistic regression models in order to assess

the impact of several variables on SRH. We assume that the latent health variable is measured

by education4, objective health5 and by some other individual characteristics in the following

way:

Where ß’s are the vectors of the coefficients and is random term capturing unmeasured and

immeasurable effects on the true health status. And X contains the variables i.e. education,

occupation or personal characteristic. The subjective health status is taken as dependent

variable in the model. As the response variable is measured on ordinal scale, so it is going to

be treated as ordinal under the assumption that the levels of SRH have natural ordering

ranges from ‘excellent’ to ‘poor’, but the distance between adjacent levels are unknown. So

for this purpose we have again recoded the data in the following way; ‘‘5 excellent, 4 good, 3

fair, 2 poor and 1 very poor’’ for response variable.

4 Generally the education is included in the model by years of schooling but for this study we use it as dummy variable. 5 Objective health is also considered as past health.

5.1 Estimation Results

Specifically, our objective is to estimate the effect of education on health, holding other

factors constant. Therefore, taking a closer look at the possible factors and identifying

whether they play any significant role in determining the health status. Table 5.1 presents the

results from the estimation of model I to III, model I contains schooling variables, age and

gender. Beside education at all levels, gender and age also play a significant role. However, it

is interesting to note that the only age group significant here is of 20-29 years old

respondents.

Model II contains education, employment level, and social status indicating that the

employment level and the socio-economic status play a significant role in determining the

subjective health status. Model III is an extension of the model II by including additional

individual characteristics i.e. age, gender, marital status, residence type and province.

Although controlling for these variables does not alter the significance of education in

determining the self reported health (SRH) but nevertheless the significance of employment

status and socio-economic status has changed. Purpose of adding more socio-economic

variables in the model II and model III is to make sure that our model should not be

misperceived. The marital status and residence type are statistically significant.

Table 5.1: Ordered logit estimates for health status Explanatory variables MODEL I MODEL II MODEL III

Coefficients Coefficients Coefficients

(Std. error)

p-value (Std. error)

p-value (Std. error)

p-value

-1.275 -1.142 -.868 < primary

(.213) .000

(.219) .000

(.231) .000

-1.487 -1.331 -.925 Primary but < middle

(.166) .000

(.173) .000

(.183) .000

-1.330 -1.207 -.671 Middle but < matric

(.174) .000

(.178) .000

(.187) .000

-.933 -.858 -.483 Matric but < degree

(.164) .000

(.165) .000

(.172) .005

-.534 .035 Employed

(.143 ) .000

(.164) .832

-.525 .016 Self Employed

(.139) .000

(.164) .924

-.920 -.790 Unemployed

(.346) .008

(.366) .031

-.581 .209 Student

(.234) .013

(.250) .404

-1.729 .005 work in home/ Sick

(.150) .000

(.232) .983

-.770 .004 -.118 .725 Disability (.270) .019 (.335) .036

.211 .209 (.090) (.100)

Social economic status

Higher

-.038 .702 Urban (.100) .000

-1.062

Punjab (.189) 1.227

Sindh (.201)

.000

1.526 NWFP

(.209) .000

-1.301 -1.501 Male

(0.1) 0.000

(.186) .000

.231 Single

(.305) .450

.285 Married

(.276) .301

-.557 .589 Age 20 - 29 years

(.200) .005

(.256) .021

-.247 .234 Age 35 - 39 years

(.205) .227

(.249) .348

-.014 -.146 Age 40 - 49 years

(.216) .947

(.258) .571

0-.163 -.178 Age 50 - 59 years

(.233) .483

(.264) .499

-2 Log Likelihood 580.095 706.581 2775.392 Chi Square 287.654 268.913 866.303 Sig. 0.000 0.000 0.000 Pseudo R-square 0.154 0.144 0.4

Table 5.2: Ordered logit estimates for health status Explanatory Variables MODEL-IV

Coefficients S.E p-value

< primary -0.952 0.224 0 Primary but <middle -1.141 0.176 0 Middle but < matric -1.142 0.18 0 Matric but < degree -0.826 0.167 0 Employed -0.296 0.146 0.043 Self Employed -0.336 0.142 0.018 Unemployed -0.958 0.352 0.007 Student -0.524 0.238 0.028 Economically Inactive -1.476 0.158 0 Disability -0.233 0.281 0.406

SES Higher 0.206 0.092 0.026 Urban Punjab Sindh NWFP Male Single Married

Age 20 - 29 years

Age 35 - 39 years

Age 40 - 49 years

Age 50 - 59 years Disease and conditions

Arthritis -0.895 0.149 0 Asthma -1.013 0.24 0 Diabetes -0.82 0.286 0

Hemorrhoids -0.543 0.17 .001

Tuberculosis -0.946 0.366 0.01

Heart Disease -0.562 0.312 0.072

Pain in back -0.508 0.11 0

Pain in knees 0.165 0.148 0.267

Vision Problem -0.836 0.111 0

Dental Problem -0.554 0.096 0

-2Log Likelihood 2624.119

Chi Square 504.825 Sig. 0 Pseudo R-sq 0.271

Table: 5.3-Ordered logit estimates for health status

Explanatory variables

coefficients

(Std. error) p-value

Explanatory

variables

coefficients

(Std .error) p-value

Urban -.044

(0.103) 0.670 Employed

+.035

(0.166) 0.835

Punjab -1.249

(0.196) 0.000 Self Employed

-.025

(0.166) 0.883

Sindh .976

(0.208) 0.000 Unemployed

-.862

(0.372) 0.020

NWFP 1.114

(0.217) 0.000 Student

.168

(0.253) 0.507

Male 1.387

(0.195) 0.000

work in home/

Sick -.009

(0.237) 0.968

Disabled .070

(0.343)

0.838

Single .256

(0.315) 0.416

Health Conditions

Married .422

(0.285) 0.139 Arthritis

-.775

(0.155) 0.000

Age 20-29 years .305

(0.265) 0.250 Asthma

-1.154

(0.251) 0.000

Age 35-39 years .032

(0.258) 0.902 Diabetes

-.725

(0.304) 0.017

Age 40-49 years -.215

(0.265) 0.416 Hemorrhoids

-.358

(0.178) 0.045

Age 50-59 years -.155

(0.270) 0.565 Tuberculosis

-1.187

(0.388) 0.002

SES higher -.200

(0.101) 0.048 Heart disease

-.674

(0.326) 0.039

Less than primary .751

(0.235) 0.001 Pain back

-.438

(0.116) 0.000

Primary but less than middle

.805

(0.185) 0.000 Pain knee

+.067

(0.155) 0.665

Middle but less than matric

.640

(0.189) 0.001 Vision problem

-.554

(0.118) 0.000

Matric but less than degree

.452

(0.173) 0.009 dental problem

-.314

(0.101) 0.002

-2Log Likelihood 3501.102 Chi square 1030.974

P value .000

Pseudo R sq 0.457

Table 5.2 presents the results from the estimations of model IV where education level and

past health are included as regressors. Model IV excludes the insignificant variables and

include the objective health measures i.e. disease and conditions, to study their role in

determining the self reported health status. All diseases included as the objective measure of

health except the pain in knee are significant. It is interesting to note that including the

objective health status measure (all diseases) has not hampered the significance of the levels

of employment. In model IV the various forms of employment status except the disability are

significantly affecting the self reported health measure. However, the Pseudo R2 is now

reduced. In each of the above considered models we see that by adding more variables the

effect of education on SRH remains significant.

Model V contains all socio-economic, past health and education variables. We note that

Pseudo R2 improves in model V as compared to other four possible models. Log likelihood

and chi square also support this model over other models. Table 5.3 presents the results of

model V from the estimations of an extensive model containing a large set of potential

variables. It can be observed that significance of the category having less than primary

education is almost on border line but rests of the categories are highly significant. As the

subjective health variable runs from excellent to poor, a negative sign of the estimate of the

coefficients of the explanatory variables indicates that a decrease in the level of the variable

is associated with a decrease in the quality of health.

6. Conclusion

The existing literature documents extensively on the existence of the educational differences

in health. The need for investigating whether any causal component lies in the observed

relationship between health and education has been emphasized since long. Often reported

literature largely reflects a causal effect of schooling and education on health. By analyzing

the responses to self-reported health, we get a body of empirical evidence that a variety of

socioeconomic and socio-demographic characteristics lead to perception of health, among

individuals, in a varied manner. Among several socio-economic variables, schooling, gender,

occupation, economic status and provinces are the significant determinants of self reported

health. However, schooling (education) seems to have the most significant impact on health

status. Moreover, the association both between health and education is not very sensitive to

either including or excluding the other variables. As the level of education increases the heath

of the individuals seems to be affected in positive way. It is likely that these health

differences are the result of the differences in behavior across education groups. An

exogenous increase in education causes better health among individuals. Hence, those with

more years of education can take care of the risks factors leading to health. Thus, health

policy researchers and analysts should emphasis that health and education represent a mutual

approach in improving population health.

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